Geospatial Modeling System for Performing Filtering Operations Based Upon a Sum of Differences of a Given and Neighboring Location Points and Related Methods

ABSTRACT

A geospatial modeling system may include a geospatial model database and a processor cooperating therewith for performing at least one noise filtering operation on data comprising elevations associated with respective location points. The at least noise filtering operation may include determining a respective center point elevation difference for each location point and based upon a sum of differences between elevations of a given location point and a plurality of neighboring location points.

FIELD OF THE INVENTION

The present invention relates to the field of topographical modeling,and, more particularly, to geospatial modeling systems and relatedmethods

BACKGROUND OF THE INVENTION

Topographical models of geographical areas may be used for manyapplications. For example, topographical models may be used in flightsimulators and for planning military missions. Furthermore,topographical models of man-made structures (e.g., cities) may beextremely helpful in applications such as cellular antenna placement,urban planning, disaster preparedness and analysis, and mapping, forexample.

Various types and methods for making topographical models are presentlybeing used. One common topographical model is the digital elevation map(DEM). A DEM is a sampled matrix representation of a geographical areawhich may be generated in an automated fashion by a computer. In a DEM,coordinate points are made to correspond with a height value. DEMs aretypically used for modeling terrain where the transitions betweendifferent elevations (e.g., valleys, mountains, etc.) are generallysmooth from one to a next. That is, DEMs typically model terrain as aplurality of curved surfaces and any discontinuities therebetween arethus “smoothed” over. Thus, in a typical DEN no distinct objects arepresent on the terrain.

One particularly advantageous 3D site modeling product is RealSite® fromthe present Assignee Harris Corp. RealSite® may be used to registeroverlapping images of a geographical area of interest, and extract highresolution DEMs using stereo and nadir view techniques, RealSite®provides a semi-automated process for making three-dimensional (3D)topographical models of geographical areas, including cities, that haveaccurate textures and structure boundaries. Moreover, RealSite® modelsare geospatially accurate. That is, the location of any given pointwithin the model corresponds to an actual location in the geographicalarea with very high accuracy. The data used to generate RealSite® modelsmay include aerial and satellite photography, electro-optical, infrared,and light detection and ranging (LIDAR).

Another advantageous approach for generating 3D site models is set forthin U.S. Pat. No. 6,654,690 to Rahmes et al., which is also assigned tothe present Assignee and is hereby incorporated herein in its entiretyby reference. This patent discloses an automated method for making atopographical model of an area including terrain and buildings thereonbased upon randomly spaced data of elevation versus position. The methodincludes processing the randomly spaced data to generate gridded data ofelevation versus position conforming to a predetermined position grid,processing the gridded data to distinguish building data from terraindata, and performing polygon extraction for the building data to makethe topographical model of the area including terrain and buildingsthereon.

One potentially challenging aspect of generating geospatial models suchas DEMS is distinguishing different types of geospatial data, e.g.,foliage data and building data. This is because foliage such as treesresults in noisy data (e.g., LIDAR data) because of the varying heightsand contours of the leaves, etc. Even though buildings generally providerelatively smooth data towards the centers of the buildings, the edgesof the buildings where a transition from roof to ground occurs oftenproduces noisy data as well. Moreover, foliage is often placed directlyadjacent to or overlies buildings, which makes distinguishing the twousing automated computer processing techniques particularly challenging.As a result, if an operator wants to separate foliage and building datato provide a model of just one or the other types of data, the operatormay have to manually designate foliage and buildings in a raw image datascene. However, this can be extremely time consuming and, thus, costprohibitive in many applications.

SUMMARY OF THE INVENTION

In view of the foregoing background, it is therefore an object of thepresent invention to provide a geospatial modeling system havinggeospatial data type separation features and related methods.

This and other objects, features, and advantages are provided by ageospatial modeling system which may include a geospatial model databaseand a processor. The processor may cooperate with the geospatial modeldatabase for performing at least one noise filtering operation on datacomprising elevations associated with respective location points. Moreparticularly, the at least noise filtering operation may includedetermining a respective center point elevation difference for eachlocation point and based upon a sum of differences between elevations ofa given location point and a plurality of neighboring location points.

The data may include ground data and foliage data, for example. As such,data processor may further separate the ground data from the foliagedata based upon the at least one noise filtering operation. The at leastone filtering operation may include a first loose tolerance filtering todetermine an inclusive estimate of building locations, and a secondstrict tolerance filtering to reduce false building locations. Also, theprocessor may further perform at least one edge recovery operation tocompensate for noisy building perimeters. In addition, the processor mayperform a masking operation based upon the inclusive estimate of thebuilding locations to generate masked building data. The at least onefiltering operation may further include a third filtering based upon themasked building data and the output of the second strict tolerancefiltering.

By way of example, the plurality of neighboring location pointscomprises eight neighboring location points. Furthermore, the at leastone sum of differences operation may also include determining arespective neighboring points elevation difference for each pair ofadjacent location points based upon a sum of differences betweenelevations of respective center point elevation differences for theadjacent location points. The at least one noise filtering operation mayfurther include selectively replacing foliage and building data pointswith nulls based upon the at least one sum of differences operation andan elevation difference threshold. The geospatial modeling system mayalso include a display coupled to the processor for displaying at leastone of the separated foliage and building data.

A geospatial modeling method aspect may include performing at least onenoise filtering operation on data comprising elevations associated withrespective location points using a processor. More particularly, the atleast noise filtering operation may include determining a respectivecenter point elevation difference for each location point and based upona sum of differences between elevations of a given location point and aplurality of neighboring location points.

A computer-readable medium may have computer executable modulesincluding a geospatial model database module and a processing modulecooperating therewith for performing at least one noise filteringoperation on data comprising elevations associated with respectivelocation points. Moreover, the at least noise filtering operation mayinclude determining a respective center point elevation difference foreach location point and based upon a sum of differences betweenelevations of a given location point and a plurality of neighboringlocation points.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a geospatial model system inaccordance with the invention.

FIG. 2 is a flow diagram illustrating a geospatial modeling method inaccordance with the invention for separating building and foliagegeospatial data.

FIGS. 3-5 are 3D grid views illustrating sum of difference filteringoperations in accordance with the method of FIG. 2.

FIGS. 6-15 are a series of screen prints illustrating various aspects ofthe method of FIG. 2.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Likenumbers refer to like elements throughout.

Referring initially to FIG. 1, a geospatial modeling system 20illustratively includes a geospatial model database 21 and a processor22 that may advantageously be used for separating different types ofgeospatial data, such as building and foliage data, for example. By wayof example, the processor 22 may be a central processing unit (CPU) of aPC, Mac, or other computing workstation, for example. A display 23 mayalso be coupled to the processor 22 for displaying geospatial modelingdata, as will be discussed further below. The processor 22 may beimplemented using a combination of hardware and softwarecomponents/modules to perform the various operations that will bediscussed further below, as will be appreciated by those skilled in theart.

By way of example, the geospatial data may be captured using varioustechniques such as stereo optical imagery, Light. Detecting and Ranging(LIDAR), Interferometric Synthetic Aperture Radar (IFSAR), etc.

Generally speaking, the data will be captured from overhead (e.g.,nadir) views of the geographical area of interest by airplanes,satellites, etch, as will be appreciated by those skilled in the art.However, oblique images of a geographical area of interest may also beused in addition to (or instead of) the nadir images to add additional3D detail to a geospatial model. The raw image data captured usingLIDAR, etc., may be processed upstream from the geospatial modeldatabase 21 into a desired format, such as a digital elevation model(DEM), or this may be done by the processor 22.

Turning additionally to FIGS. 2 through 15, a method for separatingfoliage data from the building data using the system 20 is nowdescribed. Initially, a DEM 50 (FIG. 6) of a given geographical area ofinterest or scene is generated, at Block 30. By way of example, theabove-described RealSite® system or the system set forth in U.S. Pat.No. 6,654,690 may be used for generating the initial DEM. Of course,other suitable approaches for generating DEMs may also be used. The DEM50 may be generated by another computer and stored in the geospatialmodel database 21, or it may be created by the processor 22 based upon“raw” geospatial data (e.g., LIDAR data, etc.) stored in the database.The DEM 50 illustratively includes terrain (i.e., ground), buildings,and foliage data. Yet, in some applications it is desirable to separateone of these types of data, such as the building or foliage data, fromthe remainder of the DEM data so that it can be viewed and/or processedindividually.

To this end, a first step of extracting ground data from foliage andbuilding data is performed by the processor 22, at Block 31, to generatefoliage and building data 51. As will be appreciated by those skilled inthe art, the foliage, building, and ground data include elevations orheights associated with respective location points or posts.

Following the ground extraction, a first filtering operation isperformed on the foliage and building data 51 using a first loosetolerance to determine an inclusive estimate of building locations 52,at Block 32. Referring more particularly to FIG. 3, the filteringoperation includes defining a center location point 45 and itsneighboring location points 46. Then, the processor 22 performs a sum ofdifferences operation which includes determining a respective centerpoint 45 elevation difference based upon a sum of differences betweenelevations of the center point and the neighboring location points 46,where:

$\begin{matrix}{{CenterDifference} = {\sum\limits_{i = {- 1}}^{1}{\sum\limits_{j = {- 1}}^{1}{{{x_{i,j} - x_{o,o}}}.}}}} & (1)\end{matrix}$

In the illustrated embodiment, eight neighboring location points 46 areused, but in other embodiments more or less neighboring location pointsmay be used. The above-described sum of differences operation isperformed for each of the foliage and building data location pointswithin the DEM. That is, each location point is defined as a center andthe sum of differences with respect to its neighboring location pointsis determined in accordance with equation (1).

The filtering operation further includes determining a respectiveneighboring points 46 elevation difference for each pair of adjacentlocation points based upon a sum of differences between elevations ofrespective center point elevation differences for the adjacent locationpoints. That is, given two adjacent location points, a sum ofdifferences is determined between the two location point elevationsrelative to the original location point elevations (FIG. 4). In thepresent example, there will be eight non-trivial neighbor differencesper each center location point, where:

$\begin{matrix}{{NeighborDifference}_{{di},{dj}} = {\sum\limits_{i = {- 1}}^{1}{\sum\limits_{j = {- 1}}^{1}{{{x_{i,j} - x_{{i + {di}},{j + {dj}}}}}.}}}} & (2)\end{matrix}$

Once the neighboring points elevation differences are determined, thensix adjacent points are identified that are not on a primary diagonal(indicated by shading in FIG. 5( a)) for a given center point 45. Theeight-neighbor difference is then determined for each of the sixadjacent points (FIG. 5( b)), as is a center difference of each set ofthe eight neighbor differences (FIG. 5( c)). A self-similarity isdetermined to be the smallest center difference, where higher valuescorrespond to larger differences.

The above-described filtering operation allows a “rough” estimation ofthe foliage in the building and foliage data DEM 51, which can then beseparated from the building data to provide the inclusive estimate ofbuilding locations 52. Stated alternatively, using a loose tolerancefiltering will identify a large portion of the foliage, but willintentionally allow some foliage data to remain (which appear as smallspots or speckles in FIG. 7) so that little or no building data isexcluded

Next, a DEM subtract operation is performed, at Block 33, in which theinclusive estimate of building locations 52 is “subtracted” from thebuilding and foliage data 51 to provide a preliminary estimate of thefoliage 53. The processor 22 may then begin edge recovery operations, asindicated by the dashed box 34 in FIG. 2, to compensate for noisybuilding perimeters. More particularly, the first edge recoveryoperation includes a null expansion on the inclusive estimate ofbuilding locations 52, at Block 35, to remove the foliage remnants(i.e., specks) therein (FIG. 9), and produce an estimate of thebuildings without specks 54.

The processor 22 may then perform a null filling operation on theestimate of buildings without specks 54 to generate a mask of buildingdata 55 (FIG. 10). That is, the null filling approximates geometricshapes of the buildings, which are shown in FIG. 11. The mask ofbuilding data 55 and the preliminary estimate of the foliage 53 are thenused to perform a point in poly filtering operation to generate animproved estimate of the foliage 56. A next edge recovery operationincludes a DEM subtract operation, namely subtracting the improvedestimate of the foliage 56 from the building and foliage data 51 to getan improved estimate of the building data, at Block 38. Theabove-described edge recovery operations may then be repeated one ormore times, depending upon the desired accuracy for a givenimplementation, to produce a final mask of building data 57 that will beused in a later step.

In addition, a second strict tolerance filtering is also performed onthe building and foliage data 51 to reduce false building locations, andthis filtering produces a second estimate of the building data 58, atBlock 39. More particularly, the second filtering operation is similarto the first filtering operation described above with reference to FIGS.3-5, but a more tight or strict tolerance is used. The relative valuesof the strict and loose tolerance thresholds used in the filteringoperations may be determined based upon factors such as the type of databeing processed, data resolution, and the desired accuracy of theresulting building and/or foliage data, for example, as will beappreciated by those skilled in the art.

A DEM subtract operation is then performed based upon the building andfoliage data 51 and the second estimate of building data 58 to provide asecond estimate of the foliage data 59, at Block 40 (FIG. 13). Anotherpoint in poly filtering operation is then performed, at Block 41, basedupon the second estimate of the foliage data 59 and the final mask ofbuilding data 57 to produce a final estimate of the foliage data 60.Then, another DEM subtract operation may be performed using the buildingand foliage data 51 and the final estimate of the foliage data 60 togenerate a final building data estimate 61, at Block 42. The processor22 may then selectively display the final separated foliage data 60 orthe final building data 61 on the display 23, or it may be stored forfurther processing or usage.

In summary, the above-described approach advantageously uses a centerlocation point difference of neighbor differences as a noise metric, aswell as an edge recovery routine to compensate for noisy buildingparameters. Furthermore, use of a loose tolerance to obtain a generalidea of where the buildings are, and then a strict tolerance to helpreduce the changes of false buildings, provides still further accuracy.However, it will be appreciated that in certain embodiments some of theabove-described operations may be omitted or performed in an orderdifferent than shown or described.

The above-described approach may advantageously provide the ability toautomatically detect and/or distinguish foliage from underlying terrainand man-made (i.e., building) structures within a DEM, and model themseparately. It may further allow modeling of foliage as 3D point (i.e.,voxels), as well as the modeling of man-made structures and terrain aspolygons.

Many modifications and other embodiments of the invention will come tothe mind of one skilled in the art having the benefit of the teachingspresented in the foregoing descriptions and the associated drawings.Therefore, it is understood that the invention is not to be limited tothe specific embodiments disclosed, and that modifications andembodiments are intended to be included within the scope of the appendedclaims.

1. A geospatial modeling system comprising: a geospatial model database; and a processor cooperating with said geospatial model database for performing at least one noise filtering operation on data comprising elevations associated with respective location points; the at least noise filtering operation comprising determining a respective center point elevation difference for each location point and based upon a sum of differences between elevations of a given location point and a plurality of neighboring location points.
 2. The geospatial modeling system of claim 1 wherein the data comprises ground data and foliage data.
 3. The geospatial modeling system of claim 2 wherein said processor further separates the ground data from the foliage data based upon the at least one noise filtering operation.
 4. The geospatial modeling system of claim 1 wherein the at least one filtering operation comprises a first loose tolerance filtering to determine an inclusive estimate of building locations, and a second strict tolerance filtering to reduce false building locations.
 5. The geospatial modeling system of claim 4 wherein said processor further performs at least one edge recovery operation to compensate for noisy building perimeters.
 6. The geospatial modeling system of claim 4 wherein said processor further performs a masking operation based upon the inclusive estimate of the building locations to generate masked building data.
 7. The geospatial modeling system of claim 6 wherein the at least one filtering operation further comprises a third filtering based upon the masked building data and the output of the second strict tolerance filtering.
 8. The geospatial modeling system of claim 1 where the plurality of neighboring location points comprises eight neighboring location points.
 9. The geospatial modeling system of claim 1 wherein the at least one sum of differences operation further comprises determining a respective neighboring points elevation difference for each pair of adjacent location points based upon a sum of differences between elevations of respective center point elevation differences for the adjacent location points.
 10. The geospatial modeling system of claim 1 wherein the at least one noise filtering operation further comprises selectively replacing foliage and building data points with nulls based upon the at least one sum of differences operation and an elevation difference threshold.
 11. The geospatial modeling system of claim 1 further comprising a display coupled to said processor for displaying at least one of the separated foliage and building data.
 12. A geospatial modeling method comprising: performing at least one noise filtering operation on data comprising elevations associated with respective location points using a processor; the at least noise filtering operation comprising determining a respective center point elevation difference for each location point and based upon a sum of differences between elevations of a given location point and a plurality of neighboring location points.
 13. The method of claim 12 wherein the data comprises ground data and foliage data.
 14. The method of claim 13 further comprising separating the ground data from the foliage data based upon the at least one noise filtering operation using the processor.
 15. The method of claim 12 wherein the at least one filtering operation comprises a first loose tolerance filtering to determine an inclusive estimate of building locations, and a second strict tolerance filtering to reduce false building locations.
 16. The method of claim 15 further comprising performing at least one edge recovery operation using the processor to compensate for noisy building perimeters.
 17. The method of claim 15 further comprising performing a masking operation using the processor based upon the inclusive estimate of the building locations to generate masked building data.
 18. The method of claim 17 wherein the at least one filtering operation further comprises a third filtering based upon the masked building data and the output of the second strict tolerance filtering.
 19. The method of claim 12 wherein the at least one sum of differences operation further comprises determining a respective neighboring points elevation difference for each pair of adjacent location points based upon a sum of differences between elevations of respective center point elevation differences for the adjacent location points.
 20. The method of claim 12 wherein the at least one noise filtering operation further comprises selectively replacing foliage and building data points with nulls based upon the at least one sum of differences operation and an elevation difference threshold.
 21. A computer-readable medium having computer executable modules comprising: a geospatial model database module; and a processing module cooperating with the geospatial model database module for performing at least one noise filtering operation on data comprising elevations associated with respective location points; the at least noise filtering operation comprising determining a respective center point elevation difference for each location point and based upon a sum of differences between elevations of a given location point and a plurality of neighboring location points.
 22. The computer-readable medium of claim 21 wherein the data comprises ground data and foliage data.
 23. The computer-readable medium of claim 22 further comprising separating the ground data from the foliage data based upon the at least one noise filtering operation.
 24. The computer-readable medium of claim 21 wherein the at least one filtering operation comprises a first loose tolerance filtering to determine an inclusive estimate of building locations, and a second strict tolerance filtering to reduce false building locations.
 25. The computer-readable medium of claim 24 wherein the processing module further performs at least one edge recovery operation to compensate for noisy building perimeters.
 26. The computer-readable medium of claim 24 wherein the processing module further performs a masking operation based upon the inclusive estimate of the building locations to generate masked building data.
 27. The computer-readable medium of claim 26 wherein the at least one filtering operation further comprises a third filtering based upon the masked building data and the output of the second strict tolerance filtering.
 28. The computer-readable medium of claim 21 wherein the at least one sum of differences operation further comprises determining a respective neighboring points elevation difference for each pair of adjacent location points based upon a sum of differences between elevations of respective center point elevation differences for the adjacent location points.
 29. The computer-readable medium of claim 21 wherein the at least one noise filtering operation further comprises selectively replacing foliage and building data points with nulls based upon the at least one sum of differences operation and an elevation difference threshold. 